Dissecting genetic and clinical determinants of obstructive and non-obstructive myocardial infarction: a southern european cohort

Introduction Myocardial infarction with non-obstructive coronary arteries (MINOCA) may arise from genetic susceptibility, particularly through mechanisms involving coronary microvascular dysfunction and thrombotic events. Behavioral triggers — such as smoking, substance use, stress, cold exposure, and certain medications — can also provoke coronary artery spasm. However, the underlying genetic contributors remain incompletely defined. Aim Identify the genetic, behavioral, and traditional risk factors associated with obstructive myocardial infarction (MI-CAD) compared with non-obstructive (MINOCA) in a Southern European population. Methods This prospective, single-centre observational cohort study included 1,633 patients fulfilling myocardial infarction criteria and admitted between January 2000 and December 2019. All underwent coronary angiography, evaluated by two interventional cardiologists using the Leaman Score; a score of 0 defined MINOCA. Traditional risk factors, behavioral variables, and biochemical markers were analysed. Peripheral DNA was genotyped using TaqMan assays for 33 CAD-related SNPs. Associations between genetic variants and traditional risk factors associated with MI-CAD were examined using bivariate and multivariate logistic regression. Results Among the 1,633 patients, 1,546 (94.7%) had MI-CAD. Compared with MINOCA, MI-CAD individuals showed significantly higher prevalence of hypertension, type 2 diabetes, and metabolic syndrome, and reported lower physical activity. In bivariate analyses, five SNPs were significantly associated with MI-CAD when compared with MINOCA group: TCF21 rs12190287 GC+CC (90.8% vs 80.5%, respectively; p=0.002), PON rs662 RR (9.4% vs 1.1%; p=0.009), CDKN2B rs4977574 GG (38.2% vs 26.4%; p=0.027), ADIPOQ rs266729 CG+GG (42.4% vs 54.0%; p=0.034), and PCSK9 rs2114580 GA+AA (45.1% vs 32.2%; p=0.018) (dominant and recessive models). After multivariate adjustment, four variants remained independently associated with MI-CAD: TCF21 GC+CC (OR=2.67;p=0.001), CDKN2B GG (OR=1.94;p=0.011), PCSK9 GA+AA (OR=1.93; p=0.008), and PON RR (OR=9.20;p=0.030). Among traditional risk factors, type 2 diabetes (p=0.017), age (p<0.0001), and male sex (p<0.0001) were also independent predictors of MI-CAD. Conclusions Patients with MI-CAD presented higher rates of cardiometabolic risk factors than those with MINOCA. Genetic variants in TCF21, CDKN2B, PCSK9, and PON emerged as independent contributors to obstructive MI.